jessicali9530/caltech256: papers linking to it

Kaggle · www.kaggle.com/datasets/jessicali9530/caltech256

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to jessicali9530/caltech256 (https://www.kaggle.com/datasets/jessicali9530/caltech256), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the Kaggle dataset jessicali9530/caltech256 in their Crossref records, the first in 2021; most of them (1) in 2024.
By category: AI (3); the journals with the most are Neural Computing and Applications (2) and Neural Networks (1).

By year#

20212024

Papers linking to jessicali9530/caltech256 by publication year
Year	Papers
2021	1
2023	1
2024	1

Journals#

Journals whose papers link to jessicali9530/caltech256 most
Journal	Papers
Neural Computing and Applications	2
Neural Networks	1

Papers linking to jessicali9530/caltech256, most cited first
Published	Title	Journal	Authors	Citations	Link
2021-07-22	A computational model of familiarity detection for natural pictures, abstract images, and random patterns: Combination of deep learning and anti-Hebbian training	Neural Networks	Yakov B. Kazanovich, Roman M Borisyuk	13	cites it
2024-02-02	A novel lightweight CNN-based error-reduced carry prediction approximate full adder design for multimedia applications	Neural Computing and Applications	R. Nishanth, Chellakkon Helen Sulochana	3	cites it
2023-10-20	Revolutionizing neural network efficiency: introducing FPAC for filter pruning via attention consistency	Neural Computing and Applications	Suja Cherukullapurath Mana, Sudha Rajesh, G. Kalaiarasi et al.	1	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). jessicali9530/caltech256: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-jessicali9530-caltech256

@misc{scholar4ai-code-kaggle-jessicali9530-caltech256,
  author = {{Scholar4AI}},
  title = {jessicali9530/caltech256: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-jessicali9530-caltech256}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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